{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {},
  "cells": [
    {
      "metadata": {},
      "source": [
        "<td>\n",
        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
        "</td>"
      ],
      "cell_type": "markdown"
    },
    {
      "metadata": {},
      "source": [
        "<td>\n",
        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/extras/classification-confusion-matrix.ipynb\" target=\"_blank\"><img\n",
        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
        "</td>\n",
        "\n",
        "<td>\n",
        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/master/examples/extras/classification-confusion-matrix.ipynb\" target=\"_blank\"><img\n",
        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
        "</td>"
      ],
      "cell_type": "markdown"
    },
    {
      "metadata": {},
      "source": [
        "# Classification Confusion matrix\n",
        "\n",
        "This notebook plots a confusion matrix for a multi-class, single label classification.\n",
        "\n",
        "It assumes that your project has a number of labels from multiple labelers, where the Annotation Type is Radio.\n",
        "\n",
        "## Installs"
      ],
      "cell_type": "markdown"
    },
    {
      "metadata": {},
      "source": [
        "!pip install -q 'labelbox[data]' scikit-learn matplotlib ipywidgets"
      ],
      "cell_type": "code",
      "outputs": [],
      "execution_count": null
    },
    {
      "metadata": {},
      "source": [
        "## Imports"
      ],
      "cell_type": "markdown"
    },
    {
      "metadata": {},
      "source": [
        "# Imports and etc\n",
        "\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.metrics import classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import ipywidgets as widgets\n",
        "import labelbox as lb\n",
        "\n",
        "def get_labelers():\n",
        "    labels = project.label_generator()\n",
        "    labelers = []\n",
        "\n",
        "    for l in labels:\n",
        "        if not l.extra['Created By'] in labelers:\n",
        "            labelers.append(l.extra['Created By'])\n",
        "\n",
        "    return labelers\n",
        "\n",
        "\n",
        "def get_paired_labels(labeler1, labeler2):\n",
        "    labels = project.label_generator()\n",
        "    l1 = []\n",
        "    l2 = []\n",
        "    label_dict = {}\n",
        "    \n",
        "    for l in labels:\n",
        "        if not l.data.uid in label_dict:\n",
        "            label_dict[l.data.uid] = {}\n",
        "\n",
        "        # Check if benchmark\n",
        "        if l.extra['Benchmark ID']:\n",
        "            label_dict[l.data.uid]['Benchmarks'] = l.annotations[0].value.answer.name\n",
        "\n",
        "        # Skip labels not created by either of our labelers\n",
        "        if l.extra['Created By'] != labeler1 and l.extra[\n",
        "                'Created By'] != labeler2:\n",
        "            continue\n",
        "\n",
        "        label_dict[l.data.uid][l.extra['Created By']] = \\\n",
        "            l.annotations[0].value.answer.name\n",
        "\n",
        "    for uid in label_dict.keys():\n",
        "        if labeler1 in label_dict[uid] and labeler2 in label_dict[uid]:\n",
        "            l1.append(label_dict[uid][labeler1])\n",
        "            l2.append(label_dict[uid][labeler2])\n",
        "\n",
        "    return l1, l2\n",
        "\n",
        "\n",
        "def get_label_dict():\n",
        "    labels = project.label_generator()\n",
        "    labelers = []\n",
        "    label_dict = {}\n",
        "\n",
        "    for l in labels:\n",
        "        if not l.data.uid in label_dict:\n",
        "            label_dict[l.data.uid] = {}\n",
        "\n",
        "        label_dict[l.data.uid][l.extra['Created By']] = \\\n",
        "            l.annotations[0].value.answer.name\n",
        "\n",
        "        if not l.extra['Created By'] in labelers:\n",
        "            labelers.append(l.extra['Created By'])\n",
        "\n",
        "    print(labelers)\n",
        "    return label_dict, labelers"
      ],
      "cell_type": "code",
      "outputs": [],
      "execution_count": null
    },
    {
      "metadata": {},
      "source": [
        "# Labelbox API + project and dataset\n",
        "\n",
        "# Add your api key\n",
        "API_KEY = None\n",
        "lb = lb.Client(api_key=API_KEY)\n",
        "\n",
        "# Add your project and dataset uids here\n",
        "project = lb.get_project('xxx')\n",
        "dataset = lb.get_dataset('xxx')"
      ],
      "cell_type": "code",
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:labelbox.client:Initializing Labelbox client at 'https://api.labelbox.com/graphql'\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "metadata": {},
      "source": [
        "## Labeler Selection"
      ],
      "cell_type": "markdown"
    },
    {
      "metadata": {},
      "source": [
        "# Selectors\n",
        "\n",
        "labelers = get_labelers()\n",
        "\n",
        "items = [\n",
        "    widgets.Dropdown(\n",
        "        options=['Benchmarks'] + labelers,\n",
        "        description='Compare this:',\n",
        "        disabled=False,\n",
        "    ),\n",
        "    widgets.Dropdown(\n",
        "        options=labelers,\n",
        "        description='Against this:',\n",
        "        disabled=False,\n",
        "    )]\n",
        "\n",
        "    \n",
        "widgets.VBox(items)"
      ],
      "cell_type": "code",
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "75e83359e11f41be95c141987f9b8aa9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(Dropdown(description='Compare this:', options=('Benchmarks', 'jbuhler@labelbox.com', 'jbuhler+m\u2026"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": null
    },
    {
      "metadata": {},
      "source": [
        "## Confusion Matrix"
      ],
      "cell_type": "markdown"
    },
    {
      "metadata": {},
      "source": [
        "# Plot Matrix\n",
        "\n",
        "l1, l2 = get_paired_labels(items[0].value, items[1].value)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15,15))\n",
        "c = ConfusionMatrixDisplay.from_predictions(l1, l2,\n",
        "                                        labels=list(set(l1)), sample_weight=None,\n",
        "                                        normalize=None, display_labels=None, include_values=True,\n",
        "                                        xticks_rotation='vertical', values_format=None,\n",
        "                                        cmap='Blues', ax=ax, colorbar=False)\n",
        "\n",
        "_ = c.ax_.set_xlabel(items[1].value), c.ax_.set_ylabel(items[0].value)\n",
        "\n",
        "cr = classification_report(\n",
        "    l1,\n",
        "    l2,\n",
        "    output_dict=False,\n",
        "    target_names=list(set(l1)),\n",
        "    zero_division=1\n",
        ")\n",
        "\n",
        "print(cr)"
      ],
      "cell_type": "code",
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         dog       1.00      0.78      0.88         9\n",
            "         cat       0.67      0.67      0.67         3\n",
            "  automobile       0.67      1.00      0.80         4\n",
            "       truck       1.00      1.00      1.00         2\n",
            "        frog       0.75      0.75      0.75         4\n",
            "       horse       0.60      0.75      0.67         4\n",
            "        deer       0.80      0.80      0.80         5\n",
            "    airplane       1.00      0.00      0.00         1\n",
            "        ship       0.75      1.00      0.86         6\n",
            "        bird       1.00      0.67      0.80         6\n",
            "\n",
            "    accuracy                           0.80        44\n",
            "   macro avg       0.82      0.74      0.72        44\n",
            "weighted avg       0.83      0.80      0.79        44\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "execution_count": null
    },
    {
      "metadata": {},
      "source": [],
      "cell_type": "code",
      "outputs": [],
      "execution_count": null
    }
  ]
}
